Journal of Pharmaceutical Innovation

, Volume 6, Issue 4, pp 249–263 | Cite as

Model-Based Control-Loop Performance of a Continuous Direct Compaction Process

  • Rohit RamachandranEmail author
  • Jeyrathan Arjunan
  • Anwesha Chaudhury
  • Marianthi G. Ierapetritou
Research Article


This study is concerned with enhanced model-based control of a continuous direct compression pharmaceutical process. The control-loop performance is assessed in silico and results obtained will be incorporated into the pilot plant facility of the continuous direct compaction process at the NSF Engineering Research Center of Rutgers University. The models used in the study are obtained via system identification from a combination of first principles-based dynamic models, experimental data, and/or literature data. The main objective of the paper is to formulate an effective control strategy at the basic/regulatory level, for the integrated continuous operation of the direct compaction process, and to maintain the process at the desired set-points, taking into account the multivariable process interactions and disturbances. Simulations show that that at very mild interactions, the proposed regulatory control strategy is able to maintain set-points at desired values. However, at moderate to high process interactions, oscillatory behavior of controlled variables is seen. The presence of disturbances also resulted in poor control-loop performance. Results also lend credence to the development of advanced control strategies in such scenarios and will be addressed in future work. Optimal control tuning parameters are obtained from a derivative-free optimization algorithm.


Model-based control Continuous processing Direct compaction Control-loop performance Pharmaceutical manufacturing 



This work is supported by the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems, through Grant NSF-ECC 0540855


  1. 1.
    Gorsek A, Glavic P. Design of batch versus continuous processes: Part 1: single-purpose equipment. Chem Eng Res Des. 1997;75:709–17.CrossRefGoogle Scholar
  2. 2.
    Leuenberger H. New trends in the production of pharmaceutical granules: batch versus continuous processing. Eur J Pharm Biopharm. 2001;52:289–98.PubMedCrossRefGoogle Scholar
  3. 3.
    Plumb K. Continous processing in the pharmaceutical industry: changing the mindset. Chem Eng Res Des. 2005;83:730–8.CrossRefGoogle Scholar
  4. 4.
    Leuenberger H, Betz G. Granulation process control—production of pharmaceutical granules: the classical batch concept and the problem of scale-up. Granulation. 2007;98:705–33.CrossRefGoogle Scholar
  5. 5.
    Leuenberger H. Scale-up in the 4th dimension in the field of granulation and drying or how to avoid classical scale-up. Powder Technol. 2003;130:225–30.CrossRefGoogle Scholar
  6. 6.
    Werani J, Grunberg M, Ober C, Leuenberger H. Semicontinuous granulation—the process of choice for the production of pharmaceutical granules. Powder Technol. 2004;140:163–8.CrossRefGoogle Scholar
  7. 7.
    Betz G, Junker-Purgin P, Leuenberger H. Batch and continuous procesing in the production of pharmaceutical granules. Pharm Dev Technol. 2003;8:289–97.PubMedCrossRefGoogle Scholar
  8. 8.
    Gernaey KV, Gani R. A model-based systems approach to pharmaceutical product-process design and analysis. Chemical Engineering Science. 2010;65:5757–69.Google Scholar
  9. 9.
    Reklaitis GV, Khinast J, Muzzio FJ. Pharmaceutical engineering science—new approaches to pharmaceutical development and manufacturing. Chem Eng Sci. 2010;65:iv–vii.CrossRefGoogle Scholar
  10. 10.
    Klatt KU, Marquardt W. Perspectives of process systems engineering—personal views from academia and industry. Comp Chem Eng. 2009;33:536–50.CrossRefGoogle Scholar
  11. 11.
    Huang J, Kaul G, Cai C, Chatlapalli R, Hernandez-Abad P, Ghosh K, Nagi A. Quality by design case study: an integrated multi-variate approach to drug product and process development. Int J Pharm. 2009;382(1–2):23–32.PubMedCrossRefGoogle Scholar
  12. 12.
    Mckenzie P, Kiang S, Tom J, Rubin E, Futran M. Can pharmaceutical process development become high tech? AIChE J. 2006;52(12):3990–4.CrossRefGoogle Scholar
  13. 13.
    Hamad ML, Bowman K, Smith N, Sheng X, Morris KR. Multi-scale pharmaceutical process understanding: from particle to powder dosage form. Chem Eng Sci. 2010;65:5625–38.CrossRefGoogle Scholar
  14. 14.
    Wang XZ, Liu L, Li R, Tweedie RJ, Primrose K, Corbett J, McNeil-Watson F. Online monitoring of nanoparticle suspensions using dynamic light scattering, ultrasound spectroscopy and process tomagraphy. Comp Aided Chem Eng. 2009;26:351–6.CrossRefGoogle Scholar
  15. 15.
    Lee M-J, Seo D-Y, Lee HE, Wang IC, Kim WS, Jeong MY, Choi GJ. In line NIR quantification of film thickness on pharmaceutical pellets during a fluid bed coating process. Int J Pharm. 2010;403:66–72.PubMedCrossRefGoogle Scholar
  16. 16.
    Chen ZP, Lovett D, Morris J. Process analytical technologies (PAT)—the impact for process systems engineering. Comp Aided Chem Eng. 2008;25:967–72.CrossRefGoogle Scholar
  17. 17.
    Ramachandran R, Rangaiah GP, Lakshminrayanan S. Data analysis, modeling and control performance enhancement of an industrial fluid catalytic cracking unit. Chem Eng Sci. 2007;62:1958–73.CrossRefGoogle Scholar
  18. 18.
    Heinrich S, Peglow M, Morl L. Unsteady and steady-state particle size distributions in batch and continuous fluidized bed granulation systems. Chem Eng J. 2002;86:223–31.CrossRefGoogle Scholar
  19. 19.
    Heinrich S, Peglow M, Ihlow M, Morl L. Particle population modeling in fluidized bed-spray granulation—analysis of the steady state and unsteady behaviour. Powder Technol. 2003;130:154–61.CrossRefGoogle Scholar
  20. 20.
    Wang FY, Ge XY, Balliu N, Cameron IT. Optimal control and operation of drum granulation processes. Chem Eng Sci. 2006;61:257–67.CrossRefGoogle Scholar
  21. 21.
    Wang FY, Cameron IT. A multi-form modelling approach to the dynamics and control of drum granulation processes. Powder Technol. 2007;179:2–11.CrossRefGoogle Scholar
  22. 22.
    Glaser T, Sanders CFW, Wang FY, Cameron IT, Ramachandran R, Litster JD, Poon JMH, Immanuel CD, Doyle III FJ. Model predictive control of drum granulation. J Process Contr. 2009;19(4):615–22.CrossRefGoogle Scholar
  23. 23.
    Hsu SH, Reklaitis GV, Venkatasubramanian V. Modeling and control of roller compaction for pharmaceutical manufacturing. part I: Process dynamics and control framework. J Pharam Innov. 2010;5:14–23.CrossRefGoogle Scholar
  24. 24.
    Hsu SH, Reklaitis GV, Venkatasubramanian V. Modeling and control of roller compaction for pharmaceutical manufacturing. Part II: Control system design. J Pharam Innov. 2010;5:24–36.CrossRefGoogle Scholar
  25. 25.
    Christofides PD. Model-based control of particulate processes. Heidelberg: Springer; 2008.Google Scholar
  26. 26.
    Christofides PD. Control of nonlinear distributed process systems: recent development and challenges. AICHE J. 2001;47:514–8.CrossRefGoogle Scholar
  27. 27.
    Christofides PD. Control of nonlinear distributed parameter systems: an overview and new research directions. AICHE J. 2002;54:341–6.Google Scholar
  28. 28.
    Ward JD, Yu C-C, Doherty MF. Plantwide dynamics and control of processes with crystallization. Comp Chem Eng. 2010;34:781–91.CrossRefGoogle Scholar
  29. 29.
    Patience DB, Rawlings JB. Particle-shape monitoring and control of crystallization processes. AIChE J. 2001;47:2125–30.CrossRefGoogle Scholar
  30. 30.
    M. Ashobi. Modeling and control of a continuous crystallization process using neural networks and model predictive control. PhD thesis, University of Saskatchewan, 1995.Google Scholar
  31. 31.
    Paengjuntuek W, Kittisupakorn P, Arpornwichanop A. Optimization and nonlinear control of a batch crystallization process. J Chin Inst Chem Eng. 2008;39:249–56.CrossRefGoogle Scholar
  32. 32.
    Xu S, Bao J. Distributed control of plantwide chemical processes. J Process Control. 2009;19:1671–87.CrossRefGoogle Scholar
  33. 33.
    Huang J, Goolcharran C, Ghosh K. A quality by design approach to investigate tablet dissolution shift upon accelerated stability by multivariate methods. Eur J Pharm Biopharm. 2011;78:141–50.PubMedCrossRefGoogle Scholar
  34. 34.
    Boukouvala F, Ramachandran R, Vanarase A, Muzzio FJ, Ierapetritou MG. Computer aided design and analysis of continuous pharmaceutical manufacturing processes. Comp Aided Chem Eng. 2011;29:216–20.CrossRefGoogle Scholar
  35. 35.
    Portillio PM, Ierapetritou MG, Tomassone S, Mc Dade C, Clancy D, Avontuur PPC, Muzzio FJ. Using compartment modeling to investigate mixing behavior of a continuous mixer. J Pharm Innov. 2008;3:161–75.CrossRefGoogle Scholar
  36. 36.
    W. Engisch, M. Ierapetritou, and F. J. Muzzio. Hopper refill of loss-in-weight feeding equipment. In Proceedings of the 2010 AIChE Annual Meeting, Salt Lake City, UT, USA, 2010.Google Scholar
  37. 37.
    Vanarase A, Muzzio FJ. Effect of operating conditions and design parameters in a continuous powder mixer. Powder Technol. 2011;208:26–36.CrossRefGoogle Scholar
  38. 38.
    Ramachandran R, Immanuel CD, Stepanek F, Litster JD, Doyle III FJ. A mechanistic model for granule breakage in population balances of granulation: theoretical kernel development and experimental validation. Chem Eng Res Des. 2009;87:598–614.CrossRefGoogle Scholar
  39. 39.
    Ramachandran R, Barton PI. Effective parameter estimation within a multi-dimensional population balance model framework. Chem Eng Sci. 2010;65:4884–93.CrossRefGoogle Scholar
  40. 40.
    Poon JMH, Ramachandran R, Sanders CFW, Glaser T, Immanuel CD, Doyle III FJ, Litster JD, Stepanek F, Wang FY, Cameron IT. Experimental validation studies on a multi-scale and mult-dimensional population balance model of batch granulation. Chem Eng Sci. 2009;64:775–86.CrossRefGoogle Scholar
  41. 41.
    Boukouvala F, Ramachandran R, Ierapetritou M, Muzzio FJ. Computational approaches for studying granular dynamics of continuous blending processes—ii. Macromol Mater Eng. 2011. doi: 10.1002/mame.201100054.
  42. 42.
    Portillio PM, Ierapetritou MG, Tomassone S, Mc Dade C, Clancy D, Avontuur PPC, Muzzio FJ. Quality by design methodology for development and scale-up of batch mixing processes. J Pharm Innov. 2008;3:258–70.CrossRefGoogle Scholar
  43. 43.
    Gao Y, Vanarase A, Muzzio F, Ierapetritou M. Characterizing continuous powder mixing using residence time distribution. Chem Eng Sci. 2011;66:417–25.CrossRefGoogle Scholar
  44. 44.
    Nokhodchi A, Ford JL, Rowe PH, Rubenstein MH. The effects of compression rate and force on the compaction properties of different viscosity grades of hydroxypropylmethyl-cellulose 2208. Int J Pharm. 1996;129:21–31.CrossRefGoogle Scholar
  45. 45.
    Zeng PC, Lovett D, Morris J. Process analytical technologies (PAT)—the impact for process systems engineering. Comp Aided Chem Eng. 2010;25:967–72.Google Scholar
  46. 46.
    Wu H, Heilweil EJ, Hussain AS, Khan MA. Process analytical technologies (pat)—effects of instrumental and compositional variables in terahertz spectral data quality to characterize pharmaceutical materials and tablets. Comp Aided Chem Eng. 2007;343:148–58.Google Scholar
  47. 47.
    Xiong ZH, Huang GH, Shao HH. Soft sensor modeling based on Gaussian processes. J Cent South Univ Technol. 2005;12:469–71.CrossRefGoogle Scholar
  48. 48.
    Velasco MV, Ford JL, Rowe P, Rajabi-Siahboomi AR. Influence of drug: hydroxypropylmethylcellulose ratio, drug and polymer particle size and compression force on the release of diclofenac sodium from hpmc tablets. J Control Release. 1999;57:75–85.PubMedCrossRefGoogle Scholar
  49. 49.
    Jouili K, Jerbi H, Braiek NB. An advanced fuzzy logic gain scheduling trajectory control for nonlinear systems. J Process Control. 2010;20:426–40.CrossRefGoogle Scholar
  50. 50.
    Ogunnaike BA, Ray WH. Process dynamics, modeling and control. London: Oxford University Press; 1994.Google Scholar
  51. 51.
    Desborough LD, Harris TJ. Performance assessment measures for univariate feedback control. Can J Chem Eng. 1992;1992:1186–97.CrossRefGoogle Scholar
  52. 52.
    Salsbury TI. Continuous-time model identification for closed loop control performance assessment. Control Eng Pract. 2007;2007:109–21.CrossRefGoogle Scholar
  53. 53.
    Huang B, Ding SX, Thornhill N. Alternative solutions to multi-variate control performance assessment problems. J Process Control. 2006;2006:457–71.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Rohit Ramachandran
    • 1
    Email author
  • Jeyrathan Arjunan
    • 1
    • 2
  • Anwesha Chaudhury
    • 1
  • Marianthi G. Ierapetritou
    • 1
  1. 1.Department of Chemical and Biochemical Engineering, RutgersThe State University of New JerseyPiscatawayUSA
  2. 2.Aspen Technology Inc.HoustonUSA

Personalised recommendations